Overview

Dataset statistics

Number of variables23
Number of observations73861
Missing cells282044
Missing cells (%)16.6%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory13.0 MiB
Average record size in memory184.0 B

Variable types

Numeric16
Text5
Categorical2

Alerts

BeerID is highly overall correlated with UserIdHigh correlation
Size(L) is highly overall correlated with BoilSizeHigh correlation
OG is highly overall correlated with FG and 3 other fieldsHigh correlation
FG is highly overall correlated with OG and 2 other fieldsHigh correlation
ABV is highly overall correlated with OG and 1 other fieldsHigh correlation
BoilSize is highly overall correlated with Size(L)High correlation
BoilGravity is highly overall correlated with OG and 3 other fieldsHigh correlation
MashThickness is highly overall correlated with BrewMethodHigh correlation
UserId is highly overall correlated with BeerIDHigh correlation
SugarScale is highly overall correlated with OG and 2 other fieldsHigh correlation
BrewMethod is highly overall correlated with MashThicknessHigh correlation
SugarScale is highly imbalanced (82.7%)Imbalance
BoilGravity has 2990 (4.0%) missing valuesMissing
MashThickness has 29864 (40.4%) missing valuesMissing
PitchRate has 39252 (53.1%) missing valuesMissing
PrimaryTemp has 22662 (30.7%) missing valuesMissing
PrimingMethod has 67101 (90.8%) missing valuesMissing
PrimingAmount has 69087 (93.5%) missing valuesMissing
UserId has 50490 (68.4%) missing valuesMissing
BeerID is uniformly distributedUniform
BeerID has unique valuesUnique
URL has unique valuesUnique
IBU has 4159 (5.6%) zerosZeros

Reproduction

Analysis started2023-11-18 22:05:04.665901
Analysis finished2023-11-18 22:05:58.819737
Duration54.15 seconds
Software versionydata-profiling vv4.6.1
Download configurationconfig.json

Variables

BeerID
Real number (ℝ)

HIGH CORRELATION  UNIFORM  UNIQUE 

Distinct73861
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean36931
Minimum1
Maximum73861
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size577.2 KiB
2023-11-18T23:05:59.028181image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3694
Q118466
median36931
Q355396
95-th percentile70168
Maximum73861
Range73860
Interquartile range (IQR)36930

Descriptive statistics

Standard deviation21321.978
Coefficient of variation (CV)0.57734636
Kurtosis-1.2
Mean36931
Median Absolute Deviation (MAD)18465
Skewness0
Sum2.7277606 × 109
Variance4.5462677 × 108
MonotonicityStrictly increasing
2023-11-18T23:05:59.257215image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
< 0.1%
49338 1
 
< 0.1%
49246 1
 
< 0.1%
49245 1
 
< 0.1%
49244 1
 
< 0.1%
49243 1
 
< 0.1%
49242 1
 
< 0.1%
49241 1
 
< 0.1%
49240 1
 
< 0.1%
49239 1
 
< 0.1%
Other values (73851) 73851
> 99.9%
ValueCountFrequency (%)
1 1
< 0.1%
2 1
< 0.1%
3 1
< 0.1%
4 1
< 0.1%
5 1
< 0.1%
6 1
< 0.1%
7 1
< 0.1%
8 1
< 0.1%
9 1
< 0.1%
10 1
< 0.1%
ValueCountFrequency (%)
73861 1
< 0.1%
73860 1
< 0.1%
73859 1
< 0.1%
73858 1
< 0.1%
73857 1
< 0.1%
73856 1
< 0.1%
73855 1
< 0.1%
73854 1
< 0.1%
73853 1
< 0.1%
73852 1
< 0.1%

Name
Text

Distinct59147
Distinct (%)80.1%
Missing2
Missing (%)< 0.1%
Memory size577.2 KiB
2023-11-18T23:05:59.692093image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length83
Median length44
Mean length16.814877
Min length1

Characters and Unicode

Total characters1241930
Distinct characters166
Distinct categories16 ?
Distinct scripts2 ?
Distinct blocks5 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique55541 ?
Unique (%)75.2%

Sample

1st rowVanilla Cream Ale
2nd rowSouthern Tier Pumking clone
3rd rowZombie Dust Clone - EXTRACT
4th rowZombie Dust Clone - ALL GRAIN
5th rowBakke Brygg Belgisk Blonde 50 l
ValueCountFrequency (%)
ale 10438
 
4.9%
ipa 9265
 
4.3%
4438
 
2.1%
pale 4369
 
2.0%
stout 4221
 
2.0%
clone 2915
 
1.4%
porter 2276
 
1.1%
red 2158
 
1.0%
saison 2129
 
1.0%
the 1949
 
0.9%
Other values (31626) 169520
79.3%
2023-11-18T23:06:00.697119image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
140990
 
11.4%
e 121894
 
9.8%
a 75085
 
6.0%
r 64729
 
5.2%
o 62113
 
5.0%
l 61823
 
5.0%
i 59152
 
4.8%
n 54303
 
4.4%
t 52959
 
4.3%
s 42448
 
3.4%
Other values (156) 506434
40.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 811751
65.4%
Uppercase Letter 232156
 
18.7%
Space Separator 140990
 
11.4%
Decimal Number 27532
 
2.2%
Other Punctuation 17204
 
1.4%
Dash Punctuation 6004
 
0.5%
Open Punctuation 2976
 
0.2%
Close Punctuation 2793
 
0.2%
Connector Punctuation 281
 
< 0.1%
Math Symbol 145
 
< 0.1%
Other values (6) 98
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 121894
15.0%
a 75085
 
9.2%
r 64729
 
8.0%
o 62113
 
7.7%
l 61823
 
7.6%
i 59152
 
7.3%
n 54303
 
6.7%
t 52959
 
6.5%
s 42448
 
5.2%
u 25132
 
3.1%
Other values (50) 192113
23.7%
Uppercase Letter
ValueCountFrequency (%)
A 33065
14.2%
P 24700
10.6%
S 22413
 
9.7%
B 21726
 
9.4%
I 17012
 
7.3%
C 14442
 
6.2%
R 10598
 
4.6%
M 9446
 
4.1%
H 9284
 
4.0%
D 7826
 
3.4%
Other values (34) 61644
26.6%
Other Punctuation
ValueCountFrequency (%)
. 5809
33.8%
' 4742
27.6%
# 1891
 
11.0%
/ 1470
 
8.5%
? 1364
 
7.9%
! 560
 
3.3%
& 474
 
2.8%
: 332
 
1.9%
" 316
 
1.8%
% 112
 
0.7%
Other values (8) 134
 
0.8%
Decimal Number
ValueCountFrequency (%)
1 6634
24.1%
2 5597
20.3%
0 5205
18.9%
5 2316
 
8.4%
3 1876
 
6.8%
7 1536
 
5.6%
6 1390
 
5.0%
4 1380
 
5.0%
8 925
 
3.4%
9 673
 
2.4%
Math Symbol
ValueCountFrequency (%)
+ 108
74.5%
| 21
 
14.5%
~ 9
 
6.2%
= 5
 
3.4%
< 1
 
0.7%
> 1
 
0.7%
Other Symbol
ValueCountFrequency (%)
° 19
82.6%
2
 
8.7%
® 1
 
4.3%
¦ 1
 
4.3%
Dash Punctuation
ValueCountFrequency (%)
- 5995
99.9%
6
 
0.1%
3
 
< 0.1%
Open Punctuation
ValueCountFrequency (%)
( 2919
98.1%
[ 55
 
1.8%
{ 2
 
0.1%
Close Punctuation
ValueCountFrequency (%)
) 2737
98.0%
] 53
 
1.9%
} 3
 
0.1%
Currency Symbol
ValueCountFrequency (%)
$ 19
86.4%
£ 2
 
9.1%
1
 
4.5%
Control
ValueCountFrequency (%)
 4
44.4%
3
33.3%
 2
22.2%
Other Number
ValueCountFrequency (%)
² 3
50.0%
³ 2
33.3%
½ 1
 
16.7%
Modifier Symbol
ValueCountFrequency (%)
` 24
82.8%
^ 5
 
17.2%
Other Letter
ValueCountFrequency (%)
º 7
77.8%
ª 2
 
22.2%
Space Separator
ValueCountFrequency (%)
140990
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 281
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1043916
84.1%
Common 198014
 
15.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 121894
 
11.7%
a 75085
 
7.2%
r 64729
 
6.2%
o 62113
 
5.9%
l 61823
 
5.9%
i 59152
 
5.7%
n 54303
 
5.2%
t 52959
 
5.1%
s 42448
 
4.1%
A 33065
 
3.2%
Other values (96) 416345
39.9%
Common
ValueCountFrequency (%)
140990
71.2%
1 6634
 
3.4%
- 5995
 
3.0%
. 5809
 
2.9%
2 5597
 
2.8%
0 5205
 
2.6%
' 4742
 
2.4%
( 2919
 
1.5%
) 2737
 
1.4%
5 2316
 
1.2%
Other values (50) 15070
 
7.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1239097
99.8%
None 2819
 
0.2%
Punctuation 11
 
< 0.1%
Letterlike Symbols 2
 
< 0.1%
Currency Symbols 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
140990
 
11.4%
e 121894
 
9.8%
a 75085
 
6.1%
r 64729
 
5.2%
o 62113
 
5.0%
l 61823
 
5.0%
i 59152
 
4.8%
n 54303
 
4.4%
t 52959
 
4.3%
s 42448
 
3.4%
Other values (86) 503601
40.6%
None
ValueCountFrequency (%)
ö 915
32.5%
ø 496
17.6%
ä 225
 
8.0%
å 217
 
7.7%
é 120
 
4.3%
è 89
 
3.2%
Ø 88
 
3.1%
á 76
 
2.7%
æ 72
 
2.6%
ü 65
 
2.3%
Other values (55) 456
16.2%
Punctuation
ValueCountFrequency (%)
6
54.5%
3
27.3%
2
 
18.2%
Letterlike Symbols
ValueCountFrequency (%)
2
100.0%
Currency Symbols
ValueCountFrequency (%)
1
100.0%

URL
Text

UNIQUE 

Distinct73861
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size577.2 KiB
2023-11-18T23:06:01.402092image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length118
Median length104
Mean length45.10184
Min length26

Characters and Unicode

Total characters3331267
Distinct characters41
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique73861 ?
Unique (%)100.0%

Sample

1st row/homebrew/recipe/view/1633/vanilla-cream-ale
2nd row/homebrew/recipe/view/16367/southern-tier-pumking-clone
3rd row/homebrew/recipe/view/5920/zombie-dust-clone-extract
4th row/homebrew/recipe/view/5916/zombie-dust-clone-all-grain
5th row/homebrew/recipe/view/89534/bakke-brygg-belgisk-blonde-50-l
ValueCountFrequency (%)
homebrew/recipe/view/1633/vanilla-cream-ale 1
 
< 0.1%
homebrew/recipe/view/8018/honey-nut-brown-ale-biab 1
 
< 0.1%
homebrew/recipe/view/5916/zombie-dust-clone-all-grain 1
 
< 0.1%
homebrew/recipe/view/89534/bakke-brygg-belgisk-blonde-50-l 1
 
< 0.1%
homebrew/recipe/view/28546/sierra-nevada-pale-ale-clone 1
 
< 0.1%
homebrew/recipe/view/37534/russian-river-pliny-the-elder-original 1
 
< 0.1%
homebrew/recipe/view/672/spotted-clown-new-glarus-spotted-cow-clone 1
 
< 0.1%
homebrew/recipe/view/3837/bells-two-hearted-clone 1
 
< 0.1%
homebrew/recipe/view/29265/chocolate-vanilla-porter 1
 
< 0.1%
homebrew/recipe/view/73890/bakke-brygg-hveteipa-25-l 1
 
< 0.1%
Other values (73851) 73851
> 99.9%
2023-11-18T23:06:03.421090image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 496251
14.9%
/ 369305
 
11.1%
i 222501
 
6.7%
r 221213
 
6.6%
w 165214
 
5.0%
- 147031
 
4.4%
o 140052
 
4.2%
p 115416
 
3.5%
c 109927
 
3.3%
b 107121
 
3.2%
Other values (31) 1237236
37.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 2348775
70.5%
Decimal Number 465583
 
14.0%
Other Punctuation 369314
 
11.1%
Dash Punctuation 147031
 
4.4%
Uppercase Letter 564
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 496251
21.1%
i 222501
 
9.5%
r 221213
 
9.4%
w 165214
 
7.0%
o 140052
 
6.0%
p 115416
 
4.9%
c 109927
 
4.7%
b 107121
 
4.6%
a 106314
 
4.5%
m 104861
 
4.5%
Other values (17) 559905
23.8%
Decimal Number
ValueCountFrequency (%)
2 59465
12.8%
3 54592
11.7%
4 52943
11.4%
5 52609
11.3%
1 49406
10.6%
0 42948
9.2%
6 40564
8.7%
7 37869
8.1%
8 37693
8.1%
9 37494
8.1%
Other Punctuation
ValueCountFrequency (%)
/ 369305
> 99.9%
? 9
 
< 0.1%
Dash Punctuation
ValueCountFrequency (%)
- 147031
100.0%
Uppercase Letter
ValueCountFrequency (%)
K 564
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 2349339
70.5%
Common 981928
29.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 496251
21.1%
i 222501
 
9.5%
r 221213
 
9.4%
w 165214
 
7.0%
o 140052
 
6.0%
p 115416
 
4.9%
c 109927
 
4.7%
b 107121
 
4.6%
a 106314
 
4.5%
m 104861
 
4.5%
Other values (18) 560469
23.9%
Common
ValueCountFrequency (%)
/ 369305
37.6%
- 147031
 
15.0%
2 59465
 
6.1%
3 54592
 
5.6%
4 52943
 
5.4%
5 52609
 
5.4%
1 49406
 
5.0%
0 42948
 
4.4%
6 40564
 
4.1%
7 37869
 
3.9%
Other values (3) 75196
 
7.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3330703
> 99.9%
None 564
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 496251
14.9%
/ 369305
 
11.1%
i 222501
 
6.7%
r 221213
 
6.6%
w 165214
 
5.0%
- 147031
 
4.4%
o 140052
 
4.2%
p 115416
 
3.5%
c 109927
 
3.3%
b 107121
 
3.2%
Other values (30) 1236672
37.1%
None
ValueCountFrequency (%)
ö 564
100.0%

Style
Text

Distinct175
Distinct (%)0.2%
Missing596
Missing (%)0.8%
Memory size577.2 KiB
2023-11-18T23:06:04.307112image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length34
Median length27
Mean length15.023053
Min length4

Characters and Unicode

Total characters1100664
Distinct characters58
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCream Ale
2nd rowHoliday/Winter Special Spiced Beer
3rd rowAmerican IPA
4th rowAmerican IPA
5th rowBelgian Blond Ale
ValueCountFrequency (%)
american 29547
17.0%
ipa 19062
 
11.0%
ale 18808
 
10.8%
pale 8936
 
5.2%
stout 5954
 
3.4%
beer 5832
 
3.4%
lager 4110
 
2.4%
belgian 3749
 
2.2%
imperial 3082
 
1.8%
specialty 3058
 
1.8%
Other values (161) 71216
41.1%
2023-11-18T23:06:05.864684image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 134850
 
12.3%
101117
 
9.2%
r 84044
 
7.6%
i 79444
 
7.2%
a 75745
 
6.9%
A 70240
 
6.4%
n 62365
 
5.7%
l 59229
 
5.4%
t 44582
 
4.1%
m 43178
 
3.9%
Other values (48) 345870
31.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 774983
70.4%
Uppercase Letter 215245
 
19.6%
Space Separator 101117
 
9.2%
Other Punctuation 5711
 
0.5%
Open Punctuation 1297
 
0.1%
Close Punctuation 1297
 
0.1%
Dash Punctuation 652
 
0.1%
Decimal Number 362
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 134850
17.4%
r 84044
10.8%
i 79444
10.3%
a 75745
9.8%
n 62365
8.0%
l 59229
7.6%
t 44582
 
5.8%
m 43178
 
5.6%
c 40590
 
5.2%
o 32658
 
4.2%
Other values (17) 118298
15.3%
Uppercase Letter
ValueCountFrequency (%)
A 70240
32.6%
P 33223
15.4%
I 24108
 
11.2%
B 22185
 
10.3%
S 20797
 
9.7%
W 7006
 
3.3%
L 6765
 
3.1%
R 4818
 
2.2%
C 4499
 
2.1%
E 4253
 
2.0%
Other values (11) 17351
 
8.1%
Decimal Number
ValueCountFrequency (%)
0 181
50.0%
8 117
32.3%
7 39
 
10.8%
6 25
 
6.9%
Other Punctuation
ValueCountFrequency (%)
/ 3678
64.4%
: 2033
35.6%
Space Separator
ValueCountFrequency (%)
101117
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1297
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1297
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 652
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 990228
90.0%
Common 110436
 
10.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 134850
13.6%
r 84044
 
8.5%
i 79444
 
8.0%
a 75745
 
7.6%
A 70240
 
7.1%
n 62365
 
6.3%
l 59229
 
6.0%
t 44582
 
4.5%
m 43178
 
4.4%
c 40590
 
4.1%
Other values (38) 295961
29.9%
Common
ValueCountFrequency (%)
101117
91.6%
/ 3678
 
3.3%
: 2033
 
1.8%
( 1297
 
1.2%
) 1297
 
1.2%
- 652
 
0.6%
0 181
 
0.2%
8 117
 
0.1%
7 39
 
< 0.1%
6 25
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1098917
99.8%
None 1747
 
0.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 134850
 
12.3%
101117
 
9.2%
r 84044
 
7.6%
i 79444
 
7.2%
a 75745
 
6.9%
A 70240
 
6.4%
n 62365
 
5.7%
l 59229
 
5.4%
t 44582
 
4.1%
m 43178
 
3.9%
Other values (45) 344123
31.3%
None
ValueCountFrequency (%)
ö 869
49.7%
ä 697
39.9%
è 181
 
10.4%

StyleID
Real number (ℝ)

Distinct176
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean60.179432
Minimum1
Maximum176
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size577.2 KiB
2023-11-18T23:06:06.484692image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile6
Q110
median35
Q3111
95-th percentile167
Maximum176
Range175
Interquartile range (IQR)101

Descriptive statistics

Standard deviation56.811462
Coefficient of variation (CV)0.94403454
Kurtosis-1.056568
Mean60.179432
Median Absolute Deviation (MAD)28
Skewness0.65600583
Sum4444913
Variance3227.5422
MonotonicityNot monotonic
2023-11-18T23:06:07.129686image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7 11940
 
16.2%
10 7581
 
10.3%
134 2617
 
3.5%
9 2277
 
3.1%
4 2038
 
2.8%
30 1753
 
2.4%
86 1478
 
2.0%
12 1268
 
1.7%
92 1204
 
1.6%
6 1152
 
1.6%
Other values (166) 40553
54.9%
ValueCountFrequency (%)
1 137
 
0.2%
2 36
 
< 0.1%
3 21
 
< 0.1%
4 2038
 
2.8%
5 341
 
0.5%
6 1152
 
1.6%
7 11940
16.2%
8 220
 
0.3%
9 2277
 
3.1%
10 7581
10.3%
ValueCountFrequency (%)
176 67
 
0.1%
175 1072
1.5%
174 155
 
0.2%
173 40
 
0.1%
172 41
 
0.1%
171 203
 
0.3%
170 919
1.2%
169 988
1.3%
168 145
 
0.2%
167 395
 
0.5%

Size(L)
Real number (ℝ)

HIGH CORRELATION 

Distinct1065
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean43.929775
Minimum1
Maximum9200
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size577.2 KiB
2023-11-18T23:06:07.610692image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile9.46
Q118.93
median20.82
Q323.66
95-th percentile58
Maximum9200
Range9199
Interquartile range (IQR)4.73

Descriptive statistics

Standard deviation180.37349
Coefficient of variation (CV)4.1059508
Kurtosis397.68751
Mean43.929775
Median Absolute Deviation (MAD)1.89
Skewness15.873067
Sum3244697.1
Variance32534.597
MonotonicityNot monotonic
2023-11-18T23:06:08.258687image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20.82 16714
22.6%
18.93 9994
 
13.5%
22.71 3459
 
4.7%
21 3020
 
4.1%
20 2628
 
3.6%
23 2499
 
3.4%
25 2411
 
3.3%
41.64 1814
 
2.5%
37.85 1431
 
1.9%
19.87 1344
 
1.8%
Other values (1055) 28547
38.6%
ValueCountFrequency (%)
1 6
 
< 0.1%
1.5 5
 
< 0.1%
1.6 1
 
< 0.1%
1.75 2
 
< 0.1%
1.8 1
 
< 0.1%
1.89 15
< 0.1%
1.9 1
 
< 0.1%
2 18
< 0.1%
2.01 1
 
< 0.1%
2.27 2
 
< 0.1%
ValueCountFrequency (%)
9200 1
< 0.1%
7800 1
< 0.1%
7476.19 1
< 0.1%
6102.08 2
< 0.1%
6000 1
< 0.1%
5850 1
< 0.1%
5163.3 1
< 0.1%
5000 2
< 0.1%
4900 1
< 0.1%
4693.91 1
< 0.1%

OG
Real number (ℝ)

HIGH CORRELATION 

Distinct2036
Distinct (%)2.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.4062661
Minimum1
Maximum34.0345
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size577.2 KiB
2023-11-18T23:06:08.701685image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1.041
Q11.051
median1.058
Q31.069
95-th percentile1.102
Maximum34.0345
Range33.0345
Interquartile range (IQR)0.018

Descriptive statistics

Standard deviation2.1969076
Coefficient of variation (CV)1.5622275
Kurtosis47.317493
Mean1.4062661
Median Absolute Deviation (MAD)0.008
Skewness6.7071501
Sum103868.22
Variance4.8264028
MonotonicityNot monotonic
2023-11-18T23:06:09.002691image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.052 2932
 
4.0%
1.05 2795
 
3.8%
1.051 2642
 
3.6%
1.053 2583
 
3.5%
1.054 2530
 
3.4%
1.056 2518
 
3.4%
1.055 2420
 
3.3%
1.06 2362
 
3.2%
1.057 2223
 
3.0%
1.049 2183
 
3.0%
Other values (2026) 48673
65.9%
ValueCountFrequency (%)
1 35
< 0.1%
1.001 10
 
< 0.1%
1.002 10
 
< 0.1%
1.003 6
 
< 0.1%
1.004 17
< 0.1%
1.005 19
< 0.1%
1.006 14
 
< 0.1%
1.007 10
 
< 0.1%
1.008 24
< 0.1%
1.009 9
 
< 0.1%
ValueCountFrequency (%)
34.0345 1
< 0.1%
32.5008 1
< 0.1%
32.2059 1
< 0.1%
31.7908 1
< 0.1%
31.4072 1
< 0.1%
31.4009 1
< 0.1%
29.5868 1
< 0.1%
28.5774 1
< 0.1%
28.3307 1
< 0.1%
28.3111 1
< 0.1%

FG
Real number (ℝ)

HIGH CORRELATION 

Distinct1958
Distinct (%)2.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.0758647
Minimum-0.003
Maximum23.4246
Zeros0
Zeros (%)0.0%
Negative1
Negative (%)< 0.1%
Memory size577.2 KiB
2023-11-18T23:06:09.251689image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum-0.003
5-th percentile1.007
Q11.011
median1.013
Q31.017
95-th percentile1.026
Maximum23.4246
Range23.4276
Interquartile range (IQR)0.006

Descriptive statistics

Standard deviation0.4325241
Coefficient of variation (CV)0.40202462
Kurtosis174.38228
Mean1.0758647
Median Absolute Deviation (MAD)0.003
Skewness9.7874616
Sum79464.443
Variance0.1870771
MonotonicityNot monotonic
2023-11-18T23:06:09.472725image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.012 7346
 
9.9%
1.013 7246
 
9.8%
1.011 6636
 
9.0%
1.014 6553
 
8.9%
1.01 5764
 
7.8%
1.015 5577
 
7.6%
1.016 4644
 
6.3%
1.009 4235
 
5.7%
1.017 3670
 
5.0%
1.018 3038
 
4.1%
Other values (1948) 19152
25.9%
ValueCountFrequency (%)
-0.003 1
< 0.1%
0.425441 1
< 0.1%
0.470666 1
< 0.1%
0.598715 1
< 0.1%
0.742709 1
< 0.1%
0.790449 1
< 0.1%
0.937516 1
< 0.1%
0.991 1
< 0.1%
0.994 1
< 0.1%
0.996 1
< 0.1%
ValueCountFrequency (%)
23.4246 1
< 0.1%
10.3414 1
< 0.1%
9.86137 1
< 0.1%
9.69613 1
< 0.1%
9.19146 1
< 0.1%
8.8661 1
< 0.1%
8.74075 1
< 0.1%
8.6929 1
< 0.1%
8.54079 1
< 0.1%
8.50502 1
< 0.1%

ABV
Real number (ℝ)

HIGH CORRELATION 

Distinct1502
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.1368651
Minimum0
Maximum54.72
Zeros27
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size577.2 KiB
2023-11-18T23:06:09.639964image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4.12
Q15.08
median5.79
Q36.83
95-th percentile9.29
Maximum54.72
Range54.72
Interquartile range (IQR)1.75

Descriptive statistics

Standard deviation1.8835101
Coefficient of variation (CV)0.30691731
Kurtosis86.836228
Mean6.1368651
Median Absolute Deviation (MAD)0.82
Skewness4.95166
Sum453274.99
Variance3.5476103
MonotonicityNot monotonic
2023-11-18T23:06:09.811965image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5.24 309
 
0.4%
5.17 306
 
0.4%
5.42 303
 
0.4%
5.21 300
 
0.4%
5.51 299
 
0.4%
5.19 298
 
0.4%
5.04 297
 
0.4%
5.06 294
 
0.4%
5.55 294
 
0.4%
5.1 293
 
0.4%
Other values (1492) 70868
95.9%
ValueCountFrequency (%)
0 27
< 0.1%
0.01 1
 
< 0.1%
0.02 1
 
< 0.1%
0.03 1
 
< 0.1%
0.04 2
 
< 0.1%
0.06 3
 
< 0.1%
0.08 4
 
< 0.1%
0.09 1
 
< 0.1%
0.1 2
 
< 0.1%
0.13 2
 
< 0.1%
ValueCountFrequency (%)
54.72 1
 
< 0.1%
53.81 1
 
< 0.1%
52.16 1
 
< 0.1%
51.34 1
 
< 0.1%
50.86 1
 
< 0.1%
50.48 1
 
< 0.1%
50.04 1
 
< 0.1%
49.96 1
 
< 0.1%
49.22 5
< 0.1%
48.8 1
 
< 0.1%

IBU
Real number (ℝ)

ZEROS 

Distinct12587
Distinct (%)17.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean44.276186
Minimum0
Maximum3409.3
Zeros4159
Zeros (%)5.6%
Negative0
Negative (%)0.0%
Memory size577.2 KiB
2023-11-18T23:06:09.988627image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q123.37
median35.77
Q356.38
95-th percentile104.68
Maximum3409.3
Range3409.3
Interquartile range (IQR)33.01

Descriptive statistics

Standard deviation42.945508
Coefficient of variation (CV)0.96994595
Kurtosis1113.4594
Mean44.276186
Median Absolute Deviation (MAD)14.8
Skewness19.193562
Sum3270283.4
Variance1844.3166
MonotonicityNot monotonic
2023-11-18T23:06:10.181738image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 4159
 
5.6%
18.89 99
 
0.1%
20.78 99
 
0.1%
23.08 96
 
0.1%
18.47 70
 
0.1%
20.99 66
 
0.1%
31.48 65
 
0.1%
16.37 64
 
0.1%
16.79 61
 
0.1%
41.97 57
 
0.1%
Other values (12577) 69025
93.5%
ValueCountFrequency (%)
0 4159
5.6%
0.01 1
 
< 0.1%
0.04 1
 
< 0.1%
0.08 1
 
< 0.1%
0.13 1
 
< 0.1%
0.14 2
 
< 0.1%
0.18 2
 
< 0.1%
0.2 1
 
< 0.1%
0.23 1
 
< 0.1%
0.3 1
 
< 0.1%
ValueCountFrequency (%)
3409.3 1
< 0.1%
2881.42 1
< 0.1%
2673.83 1
< 0.1%
2197.07 1
< 0.1%
1605.83 1
< 0.1%
1359.42 1
< 0.1%
1229.19 1
< 0.1%
1150.17 1
< 0.1%
920.05 1
< 0.1%
891.06 1
< 0.1%

Color
Real number (ℝ)

Distinct4729
Distinct (%)6.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.404989
Minimum0
Maximum186
Zeros101
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size577.2 KiB
2023-11-18T23:06:10.374959image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3.35
Q15.17
median8.44
Q316.79
95-th percentile40
Maximum186
Range186
Interquartile range (IQR)11.62

Descriptive statistics

Standard deviation11.944511
Coefficient of variation (CV)0.89104967
Kurtosis2.2010584
Mean13.404989
Median Absolute Deviation (MAD)4.16
Skewness1.5948763
Sum990105.91
Variance142.67135
MonotonicityNot monotonic
2023-11-18T23:06:10.582705image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
50 1516
 
2.1%
40 702
 
1.0%
3.59 240
 
0.3%
3.86 185
 
0.3%
5.1 173
 
0.2%
4.38 161
 
0.2%
3.98 160
 
0.2%
3.37 149
 
0.2%
3.17 146
 
0.2%
3.84 130
 
0.2%
Other values (4719) 70299
95.2%
ValueCountFrequency (%)
0 101
0.1%
0.02 2
 
< 0.1%
0.03 1
 
< 0.1%
0.11 1
 
< 0.1%
0.14 1
 
< 0.1%
0.18 2
 
< 0.1%
0.28 1
 
< 0.1%
0.31 3
 
< 0.1%
0.33 1
 
< 0.1%
0.41 1
 
< 0.1%
ValueCountFrequency (%)
186 1
< 0.1%
108.65 1
< 0.1%
95.59 1
< 0.1%
87.37 1
< 0.1%
83.96 1
< 0.1%
83.43 1
< 0.1%
81.66 1
< 0.1%
81.62 1
< 0.1%
80.4 2
< 0.1%
78.27 1
< 0.1%

BoilSize
Real number (ℝ)

HIGH CORRELATION 

Distinct1973
Distinct (%)2.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean49.724919
Minimum1
Maximum9700
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size577.2 KiB
2023-11-18T23:06:10.769745image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile9.46
Q120.82
median27.44
Q330
95-th percentile67
Maximum9700
Range9699
Interquartile range (IQR)9.18

Descriptive statistics

Standard deviation193.24643
Coefficient of variation (CV)3.8863095
Kurtosis402.33839
Mean49.724919
Median Absolute Deviation (MAD)4.56
Skewness15.856201
Sum3672732.3
Variance37344.182
MonotonicityNot monotonic
2023-11-18T23:06:10.954630image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
28.39 11040
 
14.9%
26.5 4772
 
6.5%
11.36 3828
 
5.2%
24.61 3556
 
4.8%
28.5 3102
 
4.2%
22.71 2048
 
2.8%
30.28 1728
 
2.3%
28 1505
 
2.0%
18.93 1419
 
1.9%
30 1407
 
1.9%
Other values (1963) 39456
53.4%
ValueCountFrequency (%)
1 6
 
< 0.1%
1.25 1
 
< 0.1%
1.5 3
 
< 0.1%
1.6 2
 
< 0.1%
1.7 1
 
< 0.1%
1.75 2
 
< 0.1%
1.8 1
 
< 0.1%
1.89 29
< 0.1%
2 28
< 0.1%
2.08 1
 
< 0.1%
ValueCountFrequency (%)
9700 1
< 0.1%
9130.41 1
< 0.1%
7800 1
< 0.1%
7000 1
< 0.1%
6454.13 2
< 0.1%
5850 1
< 0.1%
5400 1
< 0.1%
5320 1
< 0.1%
5280.65 1
< 0.1%
5050 1
< 0.1%

BoilTime
Real number (ℝ)

Distinct75
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean65.07487
Minimum0
Maximum240
Zeros283
Zeros (%)0.4%
Negative0
Negative (%)0.0%
Memory size577.2 KiB
2023-11-18T23:06:11.131205image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile60
Q160
median60
Q360
95-th percentile90
Maximum240
Range240
Interquartile range (IQR)0

Descriptive statistics

Standard deviation15.024228
Coefficient of variation (CV)0.23087604
Kurtosis12.59405
Mean65.07487
Median Absolute Deviation (MAD)0
Skewness1.3798972
Sum4806495
Variance225.72743
MonotonicityNot monotonic
2023-11-18T23:06:11.317261image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
60 55304
74.9%
90 11393
 
15.4%
75 2128
 
2.9%
70 1240
 
1.7%
30 713
 
1.0%
120 531
 
0.7%
45 431
 
0.6%
80 415
 
0.6%
15 297
 
0.4%
0 283
 
0.4%
Other values (65) 1126
 
1.5%
ValueCountFrequency (%)
0 283
0.4%
1 42
 
0.1%
2 2
 
< 0.1%
5 34
 
< 0.1%
6 3
 
< 0.1%
9 2
 
< 0.1%
10 53
 
0.1%
12 1
 
< 0.1%
15 297
0.4%
16 1
 
< 0.1%
ValueCountFrequency (%)
240 22
 
< 0.1%
228 1
 
< 0.1%
210 6
 
< 0.1%
200 1
 
< 0.1%
190 2
 
< 0.1%
180 64
0.1%
170 1
 
< 0.1%
160 2
 
< 0.1%
155 1
 
< 0.1%
150 34
< 0.1%

BoilGravity
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct509
Distinct (%)0.7%
Missing2990
Missing (%)4.0%
Infinite0
Infinite (%)0.0%
Mean1.3539547
Minimum0
Maximum52.6
Zeros5
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size577.2 KiB
2023-11-18T23:06:11.493222image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1.031
Q11.04
median1.047
Q31.06
95-th percentile1.123
Maximum52.6
Range52.6
Interquartile range (IQR)0.02

Descriptive statistics

Standard deviation1.9309886
Coefficient of variation (CV)1.426184
Kurtosis67.143919
Mean1.3539547
Median Absolute Deviation (MAD)0.009
Skewness7.3839482
Sum95956.127
Variance3.7287171
MonotonicityNot monotonic
2023-11-18T23:06:11.678222image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.044 2502
 
3.4%
1.042 2470
 
3.3%
1.043 2438
 
3.3%
1.041 2391
 
3.2%
1.04 2304
 
3.1%
1.045 2298
 
3.1%
1.039 2287
 
3.1%
1.046 2283
 
3.1%
1.047 2185
 
3.0%
1.048 2149
 
2.9%
Other values (499) 47564
64.4%
(Missing) 2990
 
4.0%
ValueCountFrequency (%)
0 5
 
< 0.1%
1 44
0.1%
1.001 14
 
< 0.1%
1.002 15
 
< 0.1%
1.003 22
< 0.1%
1.004 23
< 0.1%
1.005 18
< 0.1%
1.006 28
< 0.1%
1.007 31
< 0.1%
1.008 15
 
< 0.1%
ValueCountFrequency (%)
52.6 1
< 0.1%
45.9 1
< 0.1%
38.4 1
< 0.1%
38.3 2
< 0.1%
36 1
< 0.1%
33.9 1
< 0.1%
33.3 1
< 0.1%
31.2 1
< 0.1%
30.6 1
< 0.1%
28.8 1
< 0.1%

Efficiency
Real number (ℝ)

Distinct272
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean66.354881
Minimum0
Maximum100
Zeros101
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size577.2 KiB
2023-11-18T23:06:11.893220image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile35
Q165
median70
Q375
95-th percentile80
Maximum100
Range100
Interquartile range (IQR)10

Descriptive statistics

Standard deviation14.091686
Coefficient of variation (CV)0.21236849
Kurtosis1.7945565
Mean66.354881
Median Absolute Deviation (MAD)5
Skewness-1.4797278
Sum4901037.8
Variance198.57562
MonotonicityNot monotonic
2023-11-18T23:06:12.091220image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
70 21298
28.8%
75 13679
18.5%
35 8593
11.6%
65 5162
 
7.0%
80 3878
 
5.3%
60 3163
 
4.3%
72 2376
 
3.2%
68 1494
 
2.0%
73 1210
 
1.6%
85 1089
 
1.5%
Other values (262) 11919
16.1%
ValueCountFrequency (%)
0 101
0.1%
0.8 2
 
< 0.1%
1 7
 
< 0.1%
2 1
 
< 0.1%
2.4 1
 
< 0.1%
5 26
 
< 0.1%
6.3 1
 
< 0.1%
6.6 1
 
< 0.1%
7.5 1
 
< 0.1%
8 1
 
< 0.1%
ValueCountFrequency (%)
100 187
0.3%
99 11
 
< 0.1%
98 4
 
< 0.1%
97.47 1
 
< 0.1%
97 3
 
< 0.1%
96.1 1
 
< 0.1%
96 6
 
< 0.1%
95 73
 
0.1%
94 11
 
< 0.1%
93.9 1
 
< 0.1%

MashThickness
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct567
Distinct (%)1.3%
Missing29864
Missing (%)40.4%
Infinite0
Infinite (%)0.0%
Mean2.1272352
Minimum0
Maximum100
Zeros8
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size577.2 KiB
2023-11-18T23:06:12.268823image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1.25
Q11.5
median1.5
Q33
95-th percentile3.5
Maximum100
Range100
Interquartile range (IQR)1.5

Descriptive statistics

Standard deviation1.6823473
Coefficient of variation (CV)0.79086095
Kurtosis540.21714
Mean2.1272352
Median Absolute Deviation (MAD)0.25
Skewness16.851107
Sum93591.969
Variance2.8302924
MonotonicityNot monotonic
2023-11-18T23:06:12.478379image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.5 15499
21.0%
3 8312
 
11.3%
1.25 4923
 
6.7%
2.5 1864
 
2.5%
1.3 1110
 
1.5%
2 981
 
1.3%
1.33 880
 
1.2%
3.1 825
 
1.1%
1.4 760
 
1.0%
4 745
 
1.0%
Other values (557) 8098
 
11.0%
(Missing) 29864
40.4%
ValueCountFrequency (%)
0 8
< 0.1%
0.13 1
 
< 0.1%
0.22 1
 
< 0.1%
0.25 4
< 0.1%
0.3 5
< 0.1%
0.33 2
 
< 0.1%
0.368 1
 
< 0.1%
0.4 2
 
< 0.1%
0.42 1
 
< 0.1%
0.5 9
< 0.1%
ValueCountFrequency (%)
100 1
 
< 0.1%
63 1
 
< 0.1%
60 1
 
< 0.1%
55 1
 
< 0.1%
54 1
 
< 0.1%
50 3
< 0.1%
43 2
 
< 0.1%
40 6
< 0.1%
36 1
 
< 0.1%
35 2
 
< 0.1%

SugarScale
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size577.2 KiB
Specific Gravity
71959 
Plato
 
1902

Length

Max length16
Median length16
Mean length15.716738
Min length5

Characters and Unicode

Total characters1160854
Distinct characters16
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSpecific Gravity
2nd rowSpecific Gravity
3rd rowSpecific Gravity
4th rowSpecific Gravity
5th rowSpecific Gravity

Common Values

ValueCountFrequency (%)
Specific Gravity 71959
97.4%
Plato 1902
 
2.6%

Length

2023-11-18T23:06:12.647508image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-18T23:06:12.798682image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
specific 71959
49.3%
gravity 71959
49.3%
plato 1902
 
1.3%

Most occurring characters

ValueCountFrequency (%)
i 215877
18.6%
c 143918
12.4%
a 73861
 
6.4%
t 73861
 
6.4%
S 71959
 
6.2%
p 71959
 
6.2%
e 71959
 
6.2%
f 71959
 
6.2%
71959
 
6.2%
G 71959
 
6.2%
Other values (6) 221583
19.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 943075
81.2%
Uppercase Letter 145820
 
12.6%
Space Separator 71959
 
6.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i 215877
22.9%
c 143918
15.3%
a 73861
 
7.8%
t 73861
 
7.8%
p 71959
 
7.6%
e 71959
 
7.6%
f 71959
 
7.6%
r 71959
 
7.6%
v 71959
 
7.6%
y 71959
 
7.6%
Other values (2) 3804
 
0.4%
Uppercase Letter
ValueCountFrequency (%)
S 71959
49.3%
G 71959
49.3%
P 1902
 
1.3%
Space Separator
ValueCountFrequency (%)
71959
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1088895
93.8%
Common 71959
 
6.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
i 215877
19.8%
c 143918
13.2%
a 73861
 
6.8%
t 73861
 
6.8%
S 71959
 
6.6%
p 71959
 
6.6%
e 71959
 
6.6%
f 71959
 
6.6%
G 71959
 
6.6%
r 71959
 
6.6%
Other values (5) 149624
13.7%
Common
ValueCountFrequency (%)
71959
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1160854
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
i 215877
18.6%
c 143918
12.4%
a 73861
 
6.4%
t 73861
 
6.4%
S 71959
 
6.2%
p 71959
 
6.2%
e 71959
 
6.2%
f 71959
 
6.2%
71959
 
6.2%
G 71959
 
6.2%
Other values (6) 221583
19.1%

BrewMethod
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size577.2 KiB
All Grain
49692 
BIAB
12016 
extract
8626 
Partial Mash
 
3527

Length

Max length12
Median length9
Mean length8.0962619
Min length4

Characters and Unicode

Total characters597998
Distinct characters18
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAll Grain
2nd rowAll Grain
3rd rowextract
4th rowAll Grain
5th rowAll Grain

Common Values

ValueCountFrequency (%)
All Grain 49692
67.3%
BIAB 12016
 
16.3%
extract 8626
 
11.7%
Partial Mash 3527
 
4.8%

Length

2023-11-18T23:06:12.951543image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-18T23:06:13.122584image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
all 49692
39.1%
grain 49692
39.1%
biab 12016
 
9.5%
extract 8626
 
6.8%
partial 3527
 
2.8%
mash 3527
 
2.8%

Most occurring characters

ValueCountFrequency (%)
l 102911
17.2%
a 68899
11.5%
r 61845
10.3%
A 61708
10.3%
53219
8.9%
i 53219
8.9%
G 49692
8.3%
n 49692
8.3%
B 24032
 
4.0%
t 20779
 
3.5%
Other values (8) 52002
8.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 390277
65.3%
Uppercase Letter 154502
 
25.8%
Space Separator 53219
 
8.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
l 102911
26.4%
a 68899
17.7%
r 61845
15.8%
i 53219
13.6%
n 49692
12.7%
t 20779
 
5.3%
x 8626
 
2.2%
e 8626
 
2.2%
c 8626
 
2.2%
s 3527
 
0.9%
Uppercase Letter
ValueCountFrequency (%)
A 61708
39.9%
G 49692
32.2%
B 24032
 
15.6%
I 12016
 
7.8%
P 3527
 
2.3%
M 3527
 
2.3%
Space Separator
ValueCountFrequency (%)
53219
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 544779
91.1%
Common 53219
 
8.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
l 102911
18.9%
a 68899
12.6%
r 61845
11.4%
A 61708
11.3%
i 53219
9.8%
G 49692
9.1%
n 49692
9.1%
B 24032
 
4.4%
t 20779
 
3.8%
I 12016
 
2.2%
Other values (7) 39986
 
7.3%
Common
ValueCountFrequency (%)
53219
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 597998
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
l 102911
17.2%
a 68899
11.5%
r 61845
10.3%
A 61708
10.3%
53219
8.9%
i 53219
8.9%
G 49692
8.3%
n 49692
8.3%
B 24032
 
4.0%
t 20779
 
3.5%
Other values (8) 52002
8.7%

PitchRate
Real number (ℝ)

MISSING 

Distinct9
Distinct (%)< 0.1%
Missing39252
Missing (%)53.1%
Infinite0
Infinite (%)0.0%
Mean0.75046809
Minimum0
Maximum2
Zeros51
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size577.2 KiB
2023-11-18T23:06:13.258287image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.35
Q10.35
median0.75
Q31
95-th percentile1.5
Maximum2
Range2
Interquartile range (IQR)0.65

Descriptive statistics

Standard deviation0.39426228
Coefficient of variation (CV)0.52535515
Kurtosis0.73580178
Mean0.75046809
Median Absolute Deviation (MAD)0.25
Skewness1.0463499
Sum25972.95
Variance0.15544274
MonotonicityNot monotonic
2023-11-18T23:06:13.402901image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
0.35 9477
 
12.8%
0.75 9002
 
12.2%
0.5 5469
 
7.4%
1 5194
 
7.0%
1.25 2405
 
3.3%
1.5 1838
 
2.5%
2 640
 
0.9%
1.75 533
 
0.7%
0 51
 
0.1%
(Missing) 39252
53.1%
ValueCountFrequency (%)
0 51
 
0.1%
0.35 9477
12.8%
0.5 5469
7.4%
0.75 9002
12.2%
1 5194
7.0%
1.25 2405
 
3.3%
1.5 1838
 
2.5%
1.75 533
 
0.7%
2 640
 
0.9%
ValueCountFrequency (%)
2 640
 
0.9%
1.75 533
 
0.7%
1.5 1838
 
2.5%
1.25 2405
 
3.3%
1 5194
7.0%
0.75 9002
12.2%
0.5 5469
7.4%
0.35 9477
12.8%
0 51
 
0.1%

PrimaryTemp
Real number (ℝ)

MISSING 

Distinct217
Distinct (%)0.4%
Missing22662
Missing (%)30.7%
Infinite0
Infinite (%)0.0%
Mean19.175641
Minimum-17.78
Maximum114
Zeros19
Zeros (%)< 0.1%
Negative108
Negative (%)0.1%
Memory size577.2 KiB
2023-11-18T23:06:13.574365image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum-17.78
5-th percentile12
Q118
median20
Q320
95-th percentile23
Maximum114
Range131.78
Interquartile range (IQR)2

Descriptive statistics

Standard deviation4.2196756
Coefficient of variation (CV)0.22005396
Kurtosis63.814934
Mean19.175641
Median Absolute Deviation (MAD)1.11
Skewness3.2570417
Sum981773.62
Variance17.805662
MonotonicityNot monotonic
2023-11-18T23:06:13.763368image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20 14185
19.2%
21.11 4622
 
6.3%
18.33 4182
 
5.7%
18 4129
 
5.6%
19 2674
 
3.6%
18.89 2221
 
3.0%
19.44 1544
 
2.1%
21 1484
 
2.0%
22.22 1453
 
2.0%
17.78 1421
 
1.9%
Other values (207) 13284
18.0%
(Missing) 22662
30.7%
ValueCountFrequency (%)
-17.78 44
0.1%
-14.44 1
 
< 0.1%
-13.89 1
 
< 0.1%
-12.22 2
 
< 0.1%
-10.56 1
 
< 0.1%
-9.44 1
 
< 0.1%
-8.89 2
 
< 0.1%
-8.33 1
 
< 0.1%
-7.78 11
 
< 0.1%
-7.22 9
 
< 0.1%
ValueCountFrequency (%)
114 1
 
< 0.1%
112 1
 
< 0.1%
95 1
 
< 0.1%
85 1
 
< 0.1%
81 1
 
< 0.1%
80 4
< 0.1%
77 1
 
< 0.1%
75 3
< 0.1%
74 1
 
< 0.1%
72 3
< 0.1%

PrimingMethod
Text

MISSING 

Distinct873
Distinct (%)12.9%
Missing67101
Missing (%)90.8%
Memory size577.2 KiB
2023-11-18T23:06:14.153369image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length55
Median length47
Mean length9.2578402
Min length1

Characters and Unicode

Total characters62583
Distinct characters82
Distinct categories12 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique594 ?
Unique (%)8.8%

Sample

1st rowcorn sugar
2nd rowSukkerlake
3rd rowcorn sugar
4th rowcorn sugar
5th rowCorn Sugar
ValueCountFrequency (%)
sugar 2694
25.1%
corn 1407
13.1%
dextrose 808
 
7.5%
keg 525
 
4.9%
force 517
 
4.8%
carb 322
 
3.0%
sukkerlake 320
 
3.0%
forced 319
 
3.0%
co2 294
 
2.7%
table 272
 
2.5%
Other values (502) 3260
30.4%
2023-11-18T23:06:15.042369image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
r 7810
12.5%
e 5573
 
8.9%
a 5007
 
8.0%
o 4902
 
7.8%
4008
 
6.4%
g 3908
 
6.2%
u 3542
 
5.7%
n 3199
 
5.1%
s 2571
 
4.1%
t 2560
 
4.1%
Other values (72) 19503
31.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 49753
79.5%
Uppercase Letter 7932
 
12.7%
Space Separator 4008
 
6.4%
Decimal Number 458
 
0.7%
Other Punctuation 208
 
0.3%
Open Punctuation 59
 
0.1%
Close Punctuation 59
 
0.1%
Other Number 52
 
0.1%
Dash Punctuation 44
 
0.1%
Math Symbol 6
 
< 0.1%
Other values (2) 4
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r 7810
15.7%
e 5573
11.2%
a 5007
10.1%
o 4902
9.9%
g 3908
7.9%
u 3542
7.1%
n 3199
 
6.4%
s 2571
 
5.2%
t 2560
 
5.1%
c 2026
 
4.1%
Other values (16) 8655
17.4%
Uppercase Letter
ValueCountFrequency (%)
S 2061
26.0%
C 1898
23.9%
D 786
 
9.9%
F 724
 
9.1%
K 483
 
6.1%
B 385
 
4.9%
T 273
 
3.4%
O 256
 
3.2%
M 214
 
2.7%
E 195
 
2.5%
Other values (16) 657
 
8.3%
Other Punctuation
ValueCountFrequency (%)
/ 61
29.3%
¿ 52
25.0%
. 44
21.2%
? 31
14.9%
@ 5
 
2.4%
& 5
 
2.4%
' 3
 
1.4%
; 3
 
1.4%
: 2
 
1.0%
% 1
 
0.5%
Decimal Number
ValueCountFrequency (%)
2 335
73.1%
0 31
 
6.8%
5 24
 
5.2%
1 21
 
4.6%
3 13
 
2.8%
4 12
 
2.6%
8 10
 
2.2%
6 7
 
1.5%
7 3
 
0.7%
9 2
 
0.4%
Math Symbol
ValueCountFrequency (%)
+ 5
83.3%
~ 1
 
16.7%
Space Separator
ValueCountFrequency (%)
4008
100.0%
Open Punctuation
ValueCountFrequency (%)
( 59
100.0%
Close Punctuation
ValueCountFrequency (%)
) 59
100.0%
Other Number
ValueCountFrequency (%)
½ 52
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 44
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 3
100.0%
Modifier Symbol
ValueCountFrequency (%)
` 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 57685
92.2%
Common 4898
 
7.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
r 7810
13.5%
e 5573
 
9.7%
a 5007
 
8.7%
o 4902
 
8.5%
g 3908
 
6.8%
u 3542
 
6.1%
n 3199
 
5.5%
s 2571
 
4.5%
t 2560
 
4.4%
S 2061
 
3.6%
Other values (42) 16552
28.7%
Common
ValueCountFrequency (%)
4008
81.8%
2 335
 
6.8%
/ 61
 
1.2%
( 59
 
1.2%
) 59
 
1.2%
¿ 52
 
1.1%
½ 52
 
1.1%
- 44
 
0.9%
. 44
 
0.9%
? 31
 
0.6%
Other values (20) 153
 
3.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 62427
99.8%
None 156
 
0.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
r 7810
12.5%
e 5573
 
8.9%
a 5007
 
8.0%
o 4902
 
7.9%
4008
 
6.4%
g 3908
 
6.3%
u 3542
 
5.7%
n 3199
 
5.1%
s 2571
 
4.1%
t 2560
 
4.1%
Other values (69) 19347
31.0%
None
ValueCountFrequency (%)
ï 52
33.3%
¿ 52
33.3%
½ 52
33.3%

PrimingAmount
Text

MISSING 

Distinct1896
Distinct (%)39.7%
Missing69087
Missing (%)93.5%
Memory size577.2 KiB
2023-11-18T23:06:15.414371image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length87
Median length38
Mean length7.1524927
Min length1

Characters and Unicode

Total characters34146
Distinct characters83
Distinct categories10 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1348 ?
Unique (%)28.2%

Sample

1st row4.5 oz
2nd row6-7 g sukker/l
3rd row4.2 oz
4th row4 oz
5th row4.6 oz / .66 C
ValueCountFrequency (%)
oz 1022
 
10.5%
5 586
 
6.0%
cup 432
 
4.4%
g 335
 
3.4%
psi 308
 
3.2%
1 254
 
2.6%
6 239
 
2.5%
4 202
 
2.1%
183
 
1.9%
sukker/l 178
 
1.8%
Other values (1191) 5987
61.6%
2023-11-18T23:06:15.938186image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
5005
 
14.7%
o 1842
 
5.4%
5 1807
 
5.3%
1 1735
 
5.1%
g 1641
 
4.8%
. 1525
 
4.5%
z 1399
 
4.1%
0 1155
 
3.4%
s 1146
 
3.4%
2 1145
 
3.4%
Other values (73) 15746
46.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 15337
44.9%
Decimal Number 9599
28.1%
Space Separator 5005
 
14.7%
Other Punctuation 2653
 
7.8%
Uppercase Letter 1287
 
3.8%
Close Punctuation 84
 
0.2%
Open Punctuation 83
 
0.2%
Dash Punctuation 65
 
0.2%
Math Symbol 17
 
< 0.1%
Other Number 16
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 1842
12.0%
g 1641
10.7%
z 1399
 
9.1%
s 1146
 
7.5%
r 1075
 
7.0%
p 1062
 
6.9%
l 1002
 
6.5%
u 936
 
6.1%
e 930
 
6.1%
i 632
 
4.1%
Other values (17) 3672
23.9%
Uppercase Letter
ValueCountFrequency (%)
C 179
13.9%
S 174
13.5%
L 167
13.0%
P 151
11.7%
I 137
10.6%
O 99
7.7%
F 91
7.1%
K 66
 
5.1%
T 37
 
2.9%
D 32
 
2.5%
Other values (13) 154
12.0%
Other Punctuation
ValueCountFrequency (%)
. 1525
57.5%
/ 921
34.7%
@ 128
 
4.8%
? 39
 
1.5%
¿ 16
 
0.6%
: 6
 
0.2%
* 5
 
0.2%
; 4
 
0.2%
# 3
 
0.1%
! 2
 
0.1%
Other values (3) 4
 
0.2%
Decimal Number
ValueCountFrequency (%)
5 1807
18.8%
1 1735
18.1%
0 1155
12.0%
2 1145
11.9%
4 1000
10.4%
3 959
10.0%
6 678
 
7.1%
7 498
 
5.2%
8 386
 
4.0%
9 236
 
2.5%
Math Symbol
ValueCountFrequency (%)
+ 8
47.1%
~ 7
41.2%
= 2
 
11.8%
Close Punctuation
ValueCountFrequency (%)
) 82
97.6%
] 2
 
2.4%
Open Punctuation
ValueCountFrequency (%)
( 82
98.8%
[ 1
 
1.2%
Space Separator
ValueCountFrequency (%)
5005
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 65
100.0%
Other Number
ValueCountFrequency (%)
½ 16
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 17522
51.3%
Latin 16624
48.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 1842
 
11.1%
g 1641
 
9.9%
z 1399
 
8.4%
s 1146
 
6.9%
r 1075
 
6.5%
p 1062
 
6.4%
l 1002
 
6.0%
u 936
 
5.6%
e 930
 
5.6%
i 632
 
3.8%
Other values (40) 4959
29.8%
Common
ValueCountFrequency (%)
5005
28.6%
5 1807
 
10.3%
1 1735
 
9.9%
. 1525
 
8.7%
0 1155
 
6.6%
2 1145
 
6.5%
4 1000
 
5.7%
3 959
 
5.5%
/ 921
 
5.3%
6 678
 
3.9%
Other values (23) 1592
 
9.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 34097
99.9%
None 49
 
0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
5005
 
14.7%
o 1842
 
5.4%
5 1807
 
5.3%
1 1735
 
5.1%
g 1641
 
4.8%
. 1525
 
4.5%
z 1399
 
4.1%
0 1155
 
3.4%
s 1146
 
3.4%
2 1145
 
3.4%
Other values (69) 15697
46.0%
None
ValueCountFrequency (%)
ï 16
32.7%
¿ 16
32.7%
½ 16
32.7%
ö 1
 
2.0%

UserId
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct2785
Distinct (%)11.9%
Missing50490
Missing (%)68.4%
Infinite0
Infinite (%)0.0%
Mean43078.069
Minimum49
Maximum134362
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size577.2 KiB
2023-11-18T23:06:16.128287image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum49
5-th percentile1809
Q120984
median42897
Q357841
95-th percentile96358
Maximum134362
Range134313
Interquartile range (IQR)36857

Descriptive statistics

Standard deviation27734.253
Coefficient of variation (CV)0.64381373
Kurtosis-0.039384499
Mean43078.069
Median Absolute Deviation (MAD)19316
Skewness0.55948984
Sum1.0067776 × 109
Variance7.6918876 × 108
MonotonicityNot monotonic
2023-11-18T23:06:16.323411image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
34210 319
 
0.4%
18665 224
 
0.3%
18325 210
 
0.3%
5889 195
 
0.3%
5100 162
 
0.2%
25019 141
 
0.2%
51696 131
 
0.2%
19498 121
 
0.2%
35519 107
 
0.1%
17196 97
 
0.1%
Other values (2775) 21664
29.3%
(Missing) 50490
68.4%
ValueCountFrequency (%)
49 2
 
< 0.1%
87 6
 
< 0.1%
97 21
< 0.1%
116 3
 
< 0.1%
136 6
 
< 0.1%
145 5
 
< 0.1%
152 6
 
< 0.1%
157 1
 
< 0.1%
177 2
 
< 0.1%
196 5
 
< 0.1%
ValueCountFrequency (%)
134362 1
 
< 0.1%
133746 3
< 0.1%
133631 1
 
< 0.1%
133579 1
 
< 0.1%
133272 1
 
< 0.1%
132764 1
 
< 0.1%
132666 2
 
< 0.1%
132441 7
< 0.1%
132275 5
< 0.1%
132154 2
 
< 0.1%

Interactions

2023-11-18T23:05:54.184289image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-18T23:05:10.849540image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-18T23:05:13.473604image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-18T23:05:16.418922image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-18T23:05:19.054618image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-18T23:05:21.883450image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-18T23:05:24.443907image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-18T23:05:28.780940image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-18T23:05:31.828852image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-18T23:05:34.842092image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-18T23:05:37.876663image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-18T23:05:40.826971image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-18T23:05:43.718432image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-18T23:05:46.416717image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-18T23:05:48.997456image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-18T23:05:51.654138image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-18T23:05:54.395290image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-18T23:05:11.021463image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-18T23:05:13.633572image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-18T23:05:16.579454image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-18T23:05:19.196626image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-18T23:05:22.104234image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-18T23:05:25.046121image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-18T23:05:28.961448image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-18T23:05:32.020535image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-18T23:05:35.058144image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-18T23:05:38.044511image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-18T23:05:41.041250image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-18T23:05:43.889384image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-18T23:05:46.578643image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-18T23:05:49.169493image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-18T23:05:51.828359image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-18T23:05:54.599288image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-18T23:05:11.186977image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-18T23:05:14.015422image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-18T23:05:16.763071image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-18T23:05:19.355998image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-18T23:05:22.385233image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-18T23:05:25.287291image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-18T23:05:29.125015image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-18T23:05:32.179601image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-18T23:05:35.294180image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-18T23:05:38.214645image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-18T23:05:41.301632image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-18T23:05:44.090118image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-18T23:05:46.728644image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-18T23:05:49.335449image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-18T23:05:51.993685image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-18T23:05:54.740607image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-18T23:05:11.344037image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-18T23:05:14.167468image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-18T23:05:16.900108image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-18T23:05:19.595059image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-18T23:05:22.537235image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-18T23:05:25.493540image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-18T23:05:29.309560image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-18T23:05:32.337720image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-18T23:05:35.440172image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-18T23:05:38.361335image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-18T23:05:41.447676image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-18T23:05:44.298119image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-18T23:05:46.876509image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-18T23:05:49.483958image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-18T23:05:52.144398image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-18T23:05:54.896607image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-18T23:05:11.507502image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-18T23:05:14.321296image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-18T23:05:17.097671image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-18T23:05:19.782005image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-18T23:05:22.668233image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-18T23:05:25.659647image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-18T23:05:29.539040image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-18T23:05:32.503540image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-18T23:05:35.595278image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-18T23:05:38.643623image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-18T23:05:41.600636image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-18T23:05:44.533119image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-18T23:05:47.052470image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-18T23:05:49.638431image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-18T23:05:52.306213image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-18T23:05:55.049648image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-18T23:05:11.660726image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-18T23:05:14.466257image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-18T23:05:17.238587image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-18T23:05:19.963997image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-18T23:05:22.845232image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-18T23:05:25.809810image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-18T23:05:29.701040image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-18T23:05:32.649164image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-18T23:05:35.737972image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-18T23:05:38.808623image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-18T23:05:41.777632image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-18T23:05:44.681709image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-18T23:05:47.268464image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-18T23:05:49.783114image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-18T23:05:52.452256image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-18T23:05:55.197442image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-18T23:05:11.817801image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-18T23:05:14.620686image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-18T23:05:17.383627image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-18T23:05:20.097997image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-18T23:05:22.979234image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-18T23:05:25.955160image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-18T23:05:29.891517image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-18T23:05:32.877591image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-18T23:05:35.942633image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-18T23:05:38.959666image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-18T23:05:41.956634image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-18T23:05:44.840711image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-18T23:05:47.418503image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-18T23:05:49.935643image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-18T23:05:52.612217image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-18T23:05:55.783052image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-18T23:05:11.980654image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-18T23:05:14.781687image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-18T23:05:17.537749image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-18T23:05:20.356035image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-18T23:05:23.110555image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-18T23:05:26.118224image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-18T23:05:30.066592image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-18T23:05:33.088057image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-18T23:05:36.227909image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-18T23:05:39.121688image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-18T23:05:42.178631image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-18T23:05:45.001711image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-18T23:05:47.585463image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-18T23:05:50.097646image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-18T23:05:52.775277image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-18T23:05:55.937053image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-18T23:05:12.149948image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-18T23:05:14.946119image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-18T23:05:17.693928image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-18T23:05:20.514036image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-18T23:05:23.237599image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-18T23:05:26.273349image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-18T23:05:30.239605image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-18T23:05:33.254286image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-18T23:05:36.400797image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-18T23:05:39.280154image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-18T23:05:42.327634image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-18T23:05:45.183709image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-18T23:05:47.755807image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
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2023-11-18T23:05:52.933722image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-18T23:05:56.087347image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-18T23:05:12.314253image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-18T23:05:15.101876image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-18T23:05:17.846511image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-18T23:05:20.756434image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
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2023-11-18T23:05:26.656353image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-18T23:05:30.401775image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-18T23:05:33.407252image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
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2023-11-18T23:05:39.420153image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
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2023-11-18T23:05:45.350132image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-18T23:05:47.907826image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
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2023-11-18T23:05:12.478853image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-18T23:05:15.310841image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-18T23:05:17.992122image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-18T23:05:20.915578image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-18T23:05:23.513596image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-18T23:05:27.198994image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-18T23:05:30.542820image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-18T23:05:33.786814image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-18T23:05:36.731561image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-18T23:05:39.563559image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-18T23:05:42.631458image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-18T23:05:45.501131image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-18T23:05:48.062663image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-18T23:05:50.576122image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-18T23:05:53.242688image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-18T23:05:56.405351image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-18T23:05:12.641455image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-18T23:05:15.503385image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-18T23:05:18.137466image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-18T23:05:21.066140image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-18T23:05:23.683595image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-18T23:05:27.553527image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-18T23:05:30.745064image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-18T23:05:33.939833image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-18T23:05:36.902636image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-18T23:05:40.043965image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-18T23:05:42.778824image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-18T23:05:45.661221image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-18T23:05:48.212662image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-18T23:05:50.754115image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-18T23:05:53.397427image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-18T23:05:56.614309image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-18T23:05:12.797195image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-18T23:05:15.695289image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-18T23:05:18.279591image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-18T23:05:21.285660image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-18T23:05:23.856643image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-18T23:05:27.747703image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-18T23:05:30.924149image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-18T23:05:34.091463image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-18T23:05:37.093427image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-18T23:05:40.189019image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-18T23:05:42.928819image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-18T23:05:45.801130image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-18T23:05:48.359741image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-18T23:05:50.984113image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-18T23:05:53.545429image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-18T23:05:56.819308image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-18T23:05:12.950702image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-18T23:05:15.881966image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-18T23:05:18.414416image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-18T23:05:21.423407image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-18T23:05:24.002639image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-18T23:05:27.922640image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-18T23:05:31.074191image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-18T23:05:34.244527image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-18T23:05:37.284676image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-18T23:05:40.332510image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-18T23:05:43.063431image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-18T23:05:45.944130image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-18T23:05:48.502327image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-18T23:05:51.140145image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-18T23:05:53.687268image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-18T23:05:56.994728image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-18T23:05:13.141125image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-18T23:05:16.088052image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-18T23:05:18.578565image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-18T23:05:21.605452image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-18T23:05:24.171637image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-18T23:05:28.327269image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-18T23:05:31.362177image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-18T23:05:34.525872image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-18T23:05:37.536090image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-18T23:05:40.508488image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-18T23:05:43.292387image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-18T23:05:46.114698image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-18T23:05:48.675450image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-18T23:05:51.314607image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-18T23:05:53.870298image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-18T23:05:57.148377image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-18T23:05:13.308473image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-18T23:05:16.251308image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-18T23:05:18.877617image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-18T23:05:21.744408image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-18T23:05:24.307911image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-18T23:05:28.601782image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-18T23:05:31.620615image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-18T23:05:34.677905image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-18T23:05:37.704082image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-18T23:05:40.662380image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-18T23:05:43.559384image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-18T23:05:46.262715image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-18T23:05:48.828448image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-18T23:05:51.480606image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-18T23:05:54.030330image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Correlations

2023-11-18T23:06:16.475408image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
BeerIDStyleIDSize(L)OGFGABVIBUColorBoilSizeBoilTimeBoilGravityEfficiencyMashThicknessPitchRatePrimaryTempUserIdSugarScaleBrewMethod
BeerID1.000-0.0740.044-0.070-0.118-0.0420.010-0.0620.052-0.052-0.061-0.0210.085-0.035-0.0030.5220.0350.050
StyleID-0.0741.000-0.010-0.0550.020-0.078-0.3060.068-0.0040.090-0.0430.0230.0150.078-0.012-0.0000.0140.021
Size(L)0.044-0.0101.000-0.035-0.052-0.041-0.014-0.0670.7890.1530.1000.2730.1230.143-0.091-0.0470.1630.034
OG-0.070-0.055-0.0351.0000.7500.9010.3800.3410.0280.1090.6930.070-0.1360.1210.089-0.0570.9990.044
FG-0.1180.020-0.0520.7501.0000.4920.2490.3840.0000.0810.5410.045-0.1550.0670.041-0.1190.9210.041
ABV-0.042-0.078-0.0410.9010.4921.0000.4020.2910.0220.1070.5980.056-0.1450.1360.100-0.0240.0070.026
IBU0.010-0.306-0.0140.3800.2490.4021.0000.1930.0250.0530.254-0.054-0.0830.0120.044-0.0340.0000.000
Color-0.0620.068-0.0670.3410.3840.2910.1931.000-0.0340.0170.232-0.049-0.0680.0100.027-0.0190.0000.018
BoilSize0.052-0.0040.7890.0280.0000.0220.025-0.0341.0000.238-0.1570.3590.0480.174-0.102-0.0680.1640.034
BoilTime-0.0520.0900.1530.1090.0810.1070.0530.0170.2381.000-0.0080.1460.0260.196-0.082-0.0520.0370.134
BoilGravity-0.061-0.0430.1000.6930.5410.5980.2540.232-0.157-0.0081.000-0.081-0.0510.0360.104-0.0230.9950.051
Efficiency-0.0210.0230.2730.0700.0450.056-0.054-0.0490.3590.146-0.0811.0000.0240.151-0.105-0.0350.0970.475
MashThickness0.0850.0150.123-0.136-0.155-0.145-0.083-0.0680.0480.026-0.0510.0241.000-0.031-0.0480.2570.0001.000
PitchRate-0.0350.0780.1430.1210.0670.1360.0120.0100.1740.1960.0360.151-0.0311.000-0.245-0.0800.0130.107
PrimaryTemp-0.003-0.012-0.0910.0890.0410.1000.0440.027-0.102-0.0820.104-0.105-0.048-0.2451.0000.0320.0170.033
UserId0.522-0.000-0.047-0.057-0.119-0.024-0.034-0.019-0.068-0.052-0.023-0.0350.257-0.0800.0321.0000.0430.084
SugarScale0.0350.0140.1630.9990.9210.0070.0000.0000.1640.0370.9950.0970.0000.0130.0170.0431.0000.077
BrewMethod0.0500.0210.0340.0440.0410.0260.0000.0180.0340.1340.0510.4751.0000.1070.0330.0840.0771.000

Missing values

2023-11-18T23:05:57.422166image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
A simple visualization of nullity by column.
2023-11-18T23:05:57.928681image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-11-18T23:05:58.514636image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

BeerIDNameURLStyleStyleIDSize(L)OGFGABVIBUColorBoilSizeBoilTimeBoilGravityEfficiencyMashThicknessSugarScaleBrewMethodPitchRatePrimaryTempPrimingMethodPrimingAmountUserId
01Vanilla Cream Ale/homebrew/recipe/view/1633/vanilla-cream-aleCream Ale4521.771.0551.0135.4817.654.8328.39751.03870.0NaNSpecific GravityAll GrainNaN17.78corn sugar4.5 oz116.0
12Southern Tier Pumking clone/homebrew/recipe/view/16367/southern-tier-pumking-cloneHoliday/Winter Special Spiced Beer8520.821.0831.0218.1660.6515.6424.61601.07070.0NaNSpecific GravityAll GrainNaNNaNNaNNaN955.0
23Zombie Dust Clone - EXTRACT/homebrew/recipe/view/5920/zombie-dust-clone-extractAmerican IPA718.931.0631.0185.9159.258.9822.7160NaN70.0NaNSpecific GravityextractNaNNaNNaNNaNNaN
34Zombie Dust Clone - ALL GRAIN/homebrew/recipe/view/5916/zombie-dust-clone-all-grainAmerican IPA722.711.0611.0175.8054.488.5026.5060NaN70.0NaNSpecific GravityAll GrainNaNNaNNaNNaNNaN
45Bakke Brygg Belgisk Blonde 50 l/homebrew/recipe/view/89534/bakke-brygg-belgisk-blonde-50-lBelgian Blond Ale2050.001.0601.0106.4817.844.5760.00901.05072.0NaNSpecific GravityAll GrainNaN19.00Sukkerlake6-7 g sukker/l18325.0
56Sierra Nevada Pale Ale Clone/homebrew/recipe/view/28546/sierra-nevada-pale-ale-cloneAmerican Pale Ale1024.611.0551.0135.5840.128.0029.34701.04779.0NaNSpecific GravityAll Grain1.0NaNNaNNaN5889.0
67Russian River Pliny the Elder (original)/homebrew/recipe/view/37534/russian-river-pliny-the-elder-original-Imperial IPA8622.711.0721.0187.09268.716.3330.2890NaN75.0NaNSpecific GravityAll GrainNaNNaNNaNNaN1051.0
78Spotted Clown (New Glarus Spotted Cow clone)/homebrew/recipe/view/672/spotted-clown-new-glarus-spotted-cow-clone-Cream Ale4520.821.0541.0145.3619.975.9428.39751.04070.01.4Specific GravityAll GrainNaNNaNcorn sugar4.2 oz116.0
89Chocolate Vanilla Porter/homebrew/recipe/view/29265/chocolate-vanilla-porterRobust Porter12922.711.0601.0165.7731.6334.7630.28751.04273.0NaNSpecific GravityAll GrainNaNNaNcorn sugar4 oz116.0
910Mango Habanero IPA/homebrew/recipe/view/61082/mango-habanero-ipaImperial IPA8620.821.0801.0178.2293.028.2928.39601.05870.0NaNSpecific GravityAll GrainNaN21.11Corn Sugar4.6 oz / .66 CNaN
BeerIDNameURLStyleStyleIDSize(L)OGFGABVIBUColorBoilSizeBoilTimeBoilGravityEfficiencyMashThicknessSugarScaleBrewMethodPitchRatePrimaryTempPrimingMethodPrimingAmountUserId
7385173852Blonde Stout/homebrew/recipe/view/615556/blonde-stoutExperimental Beer6720.821.0761.0217.2452.947.2515.14601.10575.0NaNSpecific GravityPartial Mash0.7521.11Corn SugarNaNNaN
7385273853Session Simcoe/homebrew/recipe/view/618629/session-simcoeAmerican Pale Ale1021.001.0381.0084.0536.6010.0428.50601.02870.03.0Specific GravityAll GrainNaN20.00NaNNaNNaN
7385373854Chris ford wheat ipa/homebrew/recipe/view/602248/chris-ford-wheat-ipaAmerican IPA718.931.0731.0187.150.006.0122.71601.06070.0NaNSpecific GravityBIABNaNNaNNaNNaN4689.0
7385473855X Files American Ale/homebrew/recipe/view/603016/x-files-american-aleAmerican Pale Ale1018.931.0641.0166.260.009.6028.39601.04275.0NaNSpecific GravityBIABNaNNaNNaNNaNNaN
7385573856Unicorn Pee/homebrew/recipe/view/607368/unicorn-peeAmerican IPA722.711.0651.0146.7149.354.7315.14301.09855.0NaNSpecific GravityPartial Mash0.50NaNNaNNaNNaN
7385673857Amber Alfie 2/homebrew/recipe/view/609673/amber-alfie-2British Strong Ale3621.001.0521.0125.2239.736.9618.00601.06063.03.0Specific GravityAll Grain1.2520.00NaNNaN59658.0
7385773858Rye IPA/homebrew/recipe/view/610955/rye-ipaSpecialty IPA: Rye IPA15110.001.0591.0106.3759.217.9017.00601.03460.0NaNSpecific GravityBIAB0.5022.00NaNNaNNaN
7385873859SK-Kölsch/homebrew/recipe/view/586891/NaN11116.001.0461.0085.0125.423.6318.00901.04165.0NaNSpecific GravityBIAB0.3517.00sucrose140 g82450.0
7385973860Flata Rødkløver/homebrew/recipe/view/603788/Irish Red Ale9224.001.0511.0144.8225.6514.1228.00601.04372.0NaNSpecific GravityAll GrainNaN18.00Sukkerlake5 g sukker/lNaN
7386073861Elvis Juice IPA Clone/homebrew/recipe/view/613776/elvis-juice-ipa-cloneAmerican IPA720.001.0601.0106.5557.098.6512.00601.05670.0NaNSpecific GravityPartial MashNaN18.00NaNNaNNaN